1. Field of the Invention
The present invention relates to a face recognition method and apparatus, capable of recognizing a user's face to register and authenticate the user, and more particularly, to a face recognition method and apparatus capable of dividing a normalized facial image into three sections in horizontal and vertical directions, respectively, applying principal component analysis (PCA) to the divided six images to reduce the dimensions of data of the image, applying linear discriminant analysis (LDA) to the divided six images to extract characterizing vectors, and then making one vector using six similarity degree values obtained by comparing the characterizing vectors with a characterizing vector registered in advance, to identify a user through a learned support vector machine (SVM).
2. Description of the Related Art
A characteristic vector capable of identifying a user is extracted from a facial image in order to recognize the user's face. Methods of extracting a characteristic vector can use local features or overall characteristics of a face. Methods using local features extract the characteristic vector using the shapes, locations, and correlation of characteristic parts such as eyes, nose, and mouth. Methods using overall characteristics extract the characteristic vector using the entire face, usually by PCA or LDA.
However, since PCA and LDA methods project the entire facial image onto a transform matrix, they are complex and need a large memory capacity, and are difficult to use in an embedded environment or other environment where the memory capacity and processor are limited. Also, since PCA and LDA methods use the characteristics of the entire face, there is a limitation in describing a partial feature of a face.
Also, authentication methods using an SVM must be learned for each person during registration, so a system should store a plurality of people's images required for learning the SVM, and thus a large capacity memory and much time are required.